Generative AI is driving new possibilities in healthcare technology, so understanding the tech behind it is key. Two of the most commonly confused terms? Search engines and large language models (LLMs).
While both help you discover information, they work quite differently. To clear things up, we asked Brian Sharp, our Data Science Team Lead, to break it down.
“A search engine is primarily used for things like retrieving different data items, whereas an LLM could be used for things like summarizing those results. So we can actually find them, and get certain data items and then summarize them, which is typically what we refer to as RAG [retrieval-augmented generation]."
Retrieval vs. Reasoning
Let’s say you’re looking up discharge instructions for a specific diagnosis.
- A search engine will surface a list of documents containing your keywords.
- A large language model (LLM) can go a step further—by reading those documents and summarizing the key information you need.
The RAG process is one of the most powerful applications of LLMs in healthcare. You retrieve data like a search engine, then let the LLM provide the context, summary, or even draft a response.
Why It Matters for Healthcare Teams
In today’s demanding post-acute care setting, speed and accuracy aren’t just nice to have—they’re non-negotiable. The combination of search engines and LLMs enables care teams to access precise, summarized, and relevant insights from massive datasets like clinical records, care plans, or lab results.
At Forcura, we’re building AI-powered tools that harness both search and LLM technologies to make workflows smarter—and ultimately improve patient outcomes.
This post is part of our AI in Healthcare Blog Series.
💡 Explore more insights from our AI Authority experts here.
🎥 Watch Brian explain the difference between a Search Engine vs. LLM in this short video.
